USA. MTC. Motorcycle Conspicuity –What Factors Have the Greatest Impact.

Motorcycle Conspicuity – What Factors Have the Greatest Impact

Final Report June 2012

Sponsored by Iowa Department of Transportation Midwest Transportation Consortium (MTC Project 2011-01)

About the MTC The Midwest Transportation Consortium (MTC) is a Tier 1 University Transportation Center (UTC) that includes Iowa State University, the University of Iowa, and the University of Northern Iowa. The mission of the UTC program is to advance U.S. technology and expertise in the many disciplines comprising transportation through the mechanisms of education, research, and technology transfer at university-based centers of excellence. Iowa State University, through its Institute for Transportation (InTrans), is the MTC’s lead institution.

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Technical Report Documentation Page 1. Report No. MTC Project 2011-01

2. Government Accession No.

4. Title and Subtitle Motorcycle Conspicuity – What Factors Have the Greatest Impact

3. Recipient’s Catalog No.

5. Report Date June 2012 6. Performing Organization Code

7. Author(s) Mohammad Saad Shaheed, Konstantina Gkritza, and Dawn Marshall

8. Performing Organization Report No. MTC Project 2011-01

9. Performing Organization Name and Address Center for Transportation Research The National Advanced Driving and Education Simulator Iowa State University University of Iowa 2711 South Loop Drive, Suite 4700 2401 Oakdale Boulevard Ames, IA 50010-8664 Iowa City, IA 52242

10. Work Unit No. (TRAIS)

12. Sponsoring Organizations Name and Address Iowa Department of Transportation Midwest Transportation Consortium 800 Lincoln Way 2711 South Loop Drive, Suite 4700 Ames, IA 50010 Ames, IA 50010-8664

13. Type of Report and Period Covered Final Report

11. Contract or Grant No. Addendum # TS 13093

14. Sponsoring Agency Code

15. Supplementary Notes Visit www.intrans.iastate.edu for color pdfs of this and other research reports.

16. Abstract The objective of this project was to determine the effect of headlight configuration (daytime running lights, high beam, modulating) and rider color (bright yellow, blue denim, and black torso and helmet) on the conspicuity of a motorcycle to a driver of a passenger vehicle in a simulated environment. To achieve this, 36 participants completed three drives on a National Advanced Driving Simulator (NADS)-2 driving simulator.

During two of the drives, participants were presented with six oncoming motorcycles and three leading parked motorcycles, each with a different combination of rider color and headlight configuration. Each of the nine motorcycles was present in either the urban or rural driving environment.

Participants indicated when each motorcycle was first visible to them by pressing a button on the steering wheel of the driving simulator. The detection distances from the motorcycles to the participant vehicles were then recorded. Participants were within one of two groups: younger drivers (25 to 55) or older drivers (65 and older).

This research applied repeated measures analysis of variance to investigate the impact of headlight configurations and rider color on motorcycle conspicuity in urban and rural environments. The researchers found that oncoming motorcycles with modulating headlights were detected at the greatest distance compared to motorcycles with high beam or daytime running lights.

Participant ability to detect an oncoming motorcycle was also significantly influenced by the combination of headlight configurations with black or bright yellow rider colors. Leading motorcycles in urban environments were detected at a greater distance compared to those in rural environments. Leading motorcycles with riders having bright yellow clothing and helmet were detected at the greatest distance, followed by motorcycles with riders having blue denim and black rider colors. A significant interaction effect among the driving environment, rider color, and age group was also found for the detection distance of leading motorcycles.

17. Key Words driver age—driving environment—driving simulator—motorcycle headlights— motorcycle safety—motorcyclist torso—rural conspicuity—urban conspicuity

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MOTORCYCLE CONSPICUITY – WHAT FACTORS HAVE THE GREATEST IMPACT Final Report June 2012 Principal Investigators Dawn Marshall Research Associate National Advanced Driving Simulator University of Iowa Konstantina Gkritza Assistant Professor Civil, Construction, and Environmental Engineering Center for Transportation Research and Education Iowa State University Graduate Research Assistant Mohammad Saad Shaheed Authors Mohammad Saad Shaheed, Konstantina Gkritza, and Dawn Marshall

Sponsored by the Iowa Department of Transportation and the Midwest Transportation Consortium (MTC Project 2011-01) Preparation of this report was financed in part through funds provided by the Iowa Department of Transportation through its research management agreement with the University of Iowa

A report from Institute for Transportation Iowa State University 2711 South Loop Drive, Suite 4700 Ames, IA 50010-8664 Phone: 515-294-8103 Fax: 515-294-0467 www.intrans.iastate.edu

TABLE OF CONTENTS ACKNOWLEDGMENTS ............................................................................................................. ix EXECUTIVE SUMMARY ........................................................................................................... xi 1. INTRODUCTION .......................................................................................................................1 1.1 Problem Statement .........................................................................................................1 1.2 Background Information ................................................................................................1 1.3 Research Objectives and Tasks ......................................................................................2 2. LITERATURE REVIEW ............................................................................................................4 2.1 Factors Affecting Motorcycle Conspicuity ....................................................................4 2.2 Review of Recent Studies ..............................................................................................4 3. DATA COLLECTION AND DECRIPTION ..............................................................................7 3.1 Data Collection Procedure in NADS .............................................................................7 3.2 Experimental Design ......................................................................................................7 3.2.1 Independent Variables ....................................................................................7 3.2.2 Dependent Variable ........................................................................................9 3.3 Test Devices .................................................................................................................10 3.4 Recruitment Method ....................................................................................................10 3.5 Inclusion/Exclusion Criteria ........................................................................................11 3.6 Simulator Drives ..........................................................................................................11 3.7 Experimental Protocol .................................................................................................12 3.8 Discussion of Descriptive Statistics of Participant Survey Data .................................12 3.9 Driving Simulator Data ................................................................................................19 4. METHODOLOGY ....................................................................................................................23 4.1 Generalized Linear Model (GLM) Repeated Measure Analysis of Variance .............23 4.1.1 Univariate Model ..........................................................................................23 4.1.2 Multivariate Model .......................................................................................25 4.1.3 Hypothesis Testing........................................................................................25 5. RESULTS ..................................................................................................................................27 5.1 Detection Distance of Oncoming Motorcycles ............................................................27 5.2 Detection Distance of Leading Motorcycles................................................................30 6. CONCLUSIONS AND RECOMMENDATIONS ....................................................................38 6.1 Conclusions ..................................................................................................................38 6.1.1. For Oncoming Motorcycles .....................................................................................38 6.1.2 For Leading Motorcycles ..........................................................................................39 6.2 Recommendations ........................................................................................................39 REFERENCES ..............................................................................................................................41 APPENDIX A. EXPERIMENTAL PROCEDURES ....................................................................43 APPENDIX B. EMAIL SCRIPT ...................................................................................................44

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APPENDIX C. TELEPHONE SCREENING PROCEDURES.....................................................45 APPENDIX D. INFORMED CONSENT DOCUMENT ..............................................................51 APPENDIX E. DRIVING SURVEY ............................................................................................57 APPENDIX F. VISION TEST FORM ..........................................................................................63 APPENDIX G. PARTICIPANT TRAINING................................................................................66 APPENDIX H. MOTORCYCLE CONSPICUITY-RELATED FACTORS SURVEY ...............74 APPENDIX I. DEBRIEFING STATEMENT ...............................................................................76

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LIST OF FIGURES Figure 3.1. Simulated urban and rural environments .......................................................................8 Figure 3.2. Two of the six oncoming rider and headlight combinations .........................................8 Figure 3.3. The three combinations for leading parked motorcycles ..............................................8 Figure 3.4. Marital status of the study participants ........................................................................14 Figure 3.5. Employment status of the study participants ...............................................................14 Figure 3.6. Educational qualifications of the study participants ....................................................15 Figure 3.7. Yearly vehicle miles traveled by the study participants ..............................................15 Figure 3.8. Percentage of participants exceeding speed limit while driving .................................18 Figure 3.9. Participant driving comfort after drinking alcohol ......................................................18 Figure 3.10. Participant opinions about the effectiveness of different measures to improve motorcycle conspicuity ......................................................................................................19 Figure 3.11. Distribution of the detection distances for oncoming motorcycles ...........................20 Figure 3.12. Distribution of detection distances for leading motorcycles .....................................21 Figure 5.1. Detection distance as a function of headlight configurations for oncoming motorcycles (means and standard errors) ..........................................................................29 Figure 5.2. Detection distance as a function of motorcycle headlight configurations for rider colors of back and bright yellow (means and standard errors) ..........................................30 Figure 5.3. Detection distance as a function of environmental conditions (means and standard errors) .................................................................................................................................32 Figure 5.4. Detection distance as a function of rider color (means and standard errors) ..............33 Figure 5.5. Detection distance as a function of participant age group with blue denim and bright yellow riders in rural and urban environments (means and standard error) ............34 Figure 5.6. Detection distance as a function of the driving environment with bright yellow and blue denim riders for younger and older participants in rural and urban environments (means and standard error) ..........................................................................35 Figure 5.7. Detection distance as a function of rider color in rural and urban environments for older participants (means and standard error) ....................................................................36 Figure 5.8. Detection distance as a function of rider color in rural and urban environments for younger participants (means and standard error) ...............................................................37

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LIST OF TABLES Table 3.1. Summary of driving simulator experimental designs .....................................................7 Table 3.2. Primary measures (dependent variable) ........................................................................10 Table 3.3. Descriptive statistics of participant survey data ...........................................................13 Table 3.4. Participant responses about driving behavior questions from survey...........................16 Table 3.5. Participant responses about driving comfort questions from survey ............................16 Table 3.6. Participant response about driving history questions from survey ...............................17 Table 3.7. Number of responses by participants for oncoming and leading motorcycles .............20 Table 3.8. Mean and standard deviation of recorded detection distances for oncoming motorcycles given all possible experimental conditions ...................................................22 Table 3.9. Mean and standard deviation of recorded detection distances for leading motorcycles given all possible experimental conditions ...................................................22 Table 5.1. Results of multivariate tests for oncoming motorcycles ...............................................28 Table 5.2. Test of between-subject (age group) effects for oncoming motorcycles ......................28 Table 5.3. Results of univariate tests for leading motorcycles ......................................................31 Table 5.4. Test of between-subject (age group) effects for leading motorcycles ..........................31

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ACKNOWLEDGMENTS The authors would like to thank the Iowa Department of Transportation (DOT) and the Midwest Transportation Consortium (MTC) for sponsoring this research. In addition, we extend our thanks to the following individuals for their service and guidance on our technical advisory committee (TAC): Mary Stahlhut and Toni Kerkove from the Iowa DOT, Dr. Mark Wilkinson from the Vision Rehabilitation Department at the University of Iowa Hospitals and Clinics, and Ron Bandy, who is a member of the Iowa Association of Safety Education (IASE) and the Motorcycle Safety Foundation and is a certified instructor with the Iowa DOT Office of Driver Services Motorcycle Rider Education (MRE) Program.

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EXECUTIVE SUMMARY The Iowa Motorcycle Operator Manual states that when a motorcycle and another vehicle collide, more than half of these crashes are caused by drivers entering the rider’s right-of-way.

Furthermore, in crashes with motorcyclists, drivers often say they never saw the motorcycle.

Therefore, increasing motorcycle conspicuity could help address these issues, resulting in fewer crashes (and injuries and damage).

Background In 2009, the Iowa Department of Transportation (DOT) awarded a contract to the Center for Transportation Research and Education (CTRE) to study motorcycle conspicuity in Iowa using crash data analysis. That project, completed as of September 2010, reviewed previous studies on motorcycle conspicuity with a focus on the effectiveness of proposed measures for enhancing motorcycle conspicuity and examined the distribution of conspicuity-related factors in light and dark conditions in two-vehicle crashes that could potentially relate to collisions between motorcycles and other vehicles.

The limitations of examining motorcycle conspicuity by analysis of crash data were also discussed. More specifically, potential conspicuity-related factors, such as rider clothing, motorcycle color, helmet color, and motorcycle type, could not be collected from the crash database.

Driving simulator studies provide a promising avenue for the desired information to be collected in investigating motorcycle conspicuity. Driving simulators provide a safe, controlled environment in which to study situations that are hazardous in the real world. In addition, simulators enable the same situation to be presented to multiple participants, as well as multiple situations to a single participant.

Objective and Scope The objectives of this project were to determine the following:   

Impact of modulating headlight and rider clothing and helmet color on motorcycle conspicuity in both urban and rural driving environments Differences in driver awareness of motorcyclists by age (younger versus older drivers) Differences in the conspicuity of motorcycles seen from the front (oncoming motorcycles) and from the rear (leading parked motorcycles)

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Research Methodology To achieve the research objectives, 36 participants completed three drives on a National Advanced Driving Simulator (NADS)-2 driving simulator.

During two of the drives, participants were presented with six oncoming motorcycles and three leading parked motorcycles, each with a different combination of rider color and headlight configuration. Each of the nine motorcycles was present in either the urban or rural driving environment.

Participants indicated when each motorcycle was first visible to them by pressing a button on the steering wheel of the driving simulator. The detection distances from the motorcycles to the participant vehicles were recorded. Participants were within one of two groups: younger drivers (25 to 55) or older drivers (65 and older).

The recorded detection distances for the oncoming and leading parked motorcycles from the participant vehicles were then used to analyze the simulator data. Motorcycles detected at greater distances by study participants were considered more conspicuous.

This research then applied repeated measures analysis of variance to investigate the effect of headlight configurations and rider color on motorcycle conspicuity in urban and rural environments to younger and older driver (participant) groups.

For headlight configuration, the study compared daytime running lights (DRLs), high beam lamps, and modulating headlights. For rider color, the study compared bright yellow, blue denim, and black torso clothing and helmets.

The analysis was conducted separately for oncoming and leading parked motorcycles. It must be noted that the implications of the detection distances reported in this study are relative rather than absolute, given this study was conducted in a simulated environment.

Key Findings Overall, the study results revealed that motorcycles with modulating headlights had longer detection distances than high beams or DRLs for both younger and older drivers in both urban and rural environments.

The results also indicated that motorcycle riders wearing bright yellow clothing and helmets were detected at the longest distance by both younger and older drivers in both rural and urban environments.

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1. INTRODUCTION 1.1 Problem Statement Previous studies in the US and internationally suggest that low motorcycle conspicuity, or the inability of the motorcyclist to be seen by other road users, is thought to be an important factor associated with risk of motorcycle crashes. However, there has been limited research on motorcycle conspicuity in the US in the past two decades, while, at the same time, there has been a renewed interest from states in increasing motorcycle conspicuity and motorist awareness.

The Iowa Motorcycle Operator Manual states when a motorcycle and another vehicle collide, more than half of these crashes are caused by drivers entering a rider’s right-of-way.

Furthermore, in crashes with motorcyclists, drivers often say they never saw the motorcycle.

Increasing the conspicuity of the motorcycle would help address this issue.

1.2 Background Information In 2009, the Iowa Department of Transportation (DOT) awarded a contract to the Center for Transportation Research and Education (CTRE) to study motorcycle conspicuity in Iowa by analysis of crash data. That project, completed as of September 2010, reviewed previous studies on motorcycle conspicuity with a focus on the effectiveness of proposed measures for enhancing motorcycle conspicuity, compared single-and two-vehicle motorcycle crashes, and examined the distribution of conspicuity-related factors in light and dark conditions in two-vehicle crashes that could potentially relate to a collision between a motorcycle and another vehicle.

The limitations of examining motorcycle conspicuity by analysis of crash data were also discussed. More specifically, potential conspicuity-related factors, such as rider clothing, color of motorcycle, helmet color, and motorcycle type, could not be collected from the crash database.

Driving simulator studies provide a promising avenue for such information to be collected in research on motorcycle conspicuity. Driving simulators provide a safe, controlled environment in which to study situations that are hazardous in the real world. In addition, simulators enable the same situation to be presented to multiple participants as well as multiple situations to a single participant.

Simulator data collection allows both between- and within-subject experimental designs and enables consistent presentation of independent variables to allow comparisons across participant groups and situations. In a nutshell, the researchers determined that the use of a driving simulator would allow modification of factors influencing motorcycle conspicuity in this study.

This study was designed to determine the effect of two factors, headlight configuration and rider color, on the conspicuity of a motorcycle to a driver of a passenger vehicle.

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The rider color includes both the color of the torso and the helmet of a rider in this study. The researchers expected that brighter rider colors and a modulating headlight make a motorcycle more conspicuous to a driver. They also expected that some colors will increase the conspicuity of the motorcycle more than others and that some combinations may have a greater impact. In addition, the impact of headlight configuration and rider color factors on motorcycle conspicuity is expected to vary by driver age (younger versus older drivers).

The results of this study will help make informed recommendations to the Iowa DOT regarding motorcycle-related campaigns and interventions.

1.3 Research Objectives and Tasks These were the principal investigation objectives of this research:   

Impact of modulating headlight/rider color on motorcycle conspicuity in both urban and rural driving environments Differences in driver awareness of motorcyclists by age (younger versus older drivers) Differences in the conspicuity of motorcycles viewed from the front (oncoming motorcycles) and from the rear (leading motorcycles)

Thirty-six participants completed a drive on a National Advanced Driving Simulator (NADS)-2 driving simulator. During the drive, they were presented with several motorcycles in their driving environment, each with a different combination of rider colors and headlight configurations. Participants indicated when each motorcycle was first visible to them by pressing a button on or near the steering wheel of the driving simulator.

The participants were presented with both oncoming and leading motorcycles during their drive in the simulator. For oncoming motorcycles, participants were presented with a combination of headlight configurations and rider colors, while there were only a set of rider colors presented for leading motorcycles.

The two primary dependent variables, detection distance and time to encounter, were calculated based on participant button presses. The detection distance and time to encounter data were analyzed to find out which rider colors and headlight configurations, both individually and in combination, made the motorcycles in the driver’s environment the most conspicuous, as indicated by larger detection distance and longer time to encounter.

To achieve the study objectives, the following tasks were conducted.

Task 1: Establish a technical advisory committee (TAC) for the project Potential technical advisory committee (TAC) members were identified in consultation with representatives from the Iowa DOT Office of Traffic and Safety and Motorcycle Task Force. A 2

meeting of the TAC was convened within the first quarter of the project. Subsequent TAC meetings were scheduled in consultation with the project manager at the Iowa DOT as needed.

TAC members included representatives from organizations such as Iowa DOT, University of Iowa Hospitals and Clinics, and community members involved in driver and motorcycle rider education.

Task 2: Development of experimental protocol and materials The research team developed an experimental protocol for the collection of data using human subjects. Experimental materials for obtaining informed consent, experimenter scripts, and participant questionnaires were created. This task culminated with the submission of the experimental materials to the Institutional Review Board (IRB) for approval.

Task 3: Development of driving simulator scenarios The research team consulted previously-funded work that analyzed motorcycle crash data to identify urban or rural areas with a higher risk for motorcycle crashes than others. Driving scenarios were created based on the characteristics of areas with higher risk for crashes.

Within the driving scenarios, motorcycle models were created. The models were integrated into the NADS-2 driving simulator to present combinations of motorcycle conspicuity factors in both rural and urban conditions.

Task 4: Internal pilot of driving scenarios and experimental participant protocol An internal pilot was conducted to test driving scenarios and the experimental protocol. NaĂŻve staff members, who had not contributed significantly to the development of the protocol and scenarios, served as pilot participants. The pilot was followed by any necessary changes to scenarios and protocol prior to data collection with consented research participants.

Task 5: Collection of driving simulator data sampling in two age groups Experimental data were collected in the NADS research facility in Iowa City, Iowa. Thirty-six participants in two age groups consented and completed the experimental protocol, including driving the simulator and completing questionnaires. Following data collection, the data were prepared for analysis.

Task 6: Analysis of driving simulator data and preparation of final report The simulator data were analyzed using general linear model repeated measure analysis of the detection distances from the participants to the motorcycles at the time of pressing the button within the driving scenario across age groups (younger versus older), driving environments (urban versus rural), and conspicuity-related factors (headlight configurations and rider color).

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2. LITERATURE REVIEW 2.1 Factors Affecting Motorcycle Conspicuity Several studies in the literature investigate the effectiveness of various conspicuity measures in daytime and nighttime conditions. Most of these studies were performed during the 1970s and 1980s and some of were already reviewed in the final report of Enhancing Motorcycle Conspicuity Awareness in Iowa (Gkritza et al. 2010).

Quite a few studies showed that motorcycles with headlights on, flicking/dipped headlamps, or daytime running lights (DRLs) were detected at greater distances (Janoff and Casses 1971, Janoff 1973, Woltman and Austin 1973, Kirkby and Fulton 1978, Mortimer and Schuldt 1980, Hole and Tyrrell 1995, Hole et al. 1996). Other studies also revealed that riders with fluorescent waistcoats, jackets, or helmets, retroreflective garments or helmets, dark blue jackets against a light background, or yellow jackets against a dark background produced higher detection rates for motorcycles (Kirkby and Stroud 1978, Olson et al. 1979 and 1981, Fulton et al. 1980, Stroud et al. 1980, Watts 1980, Donne and Fulton 1987, Hole et al. 1996).

The next section summarizes recent studies on factors affecting motorcycle conspicuity such as daytime running lights, non-motorcycle vehicle driver age, different configurations of motorcycle front lights, error in judgment by non-motorcycle vehicle drivers, motorcycle rider clothing and motorcycle speed.

2.2 Review of Recent Studies A comprehensive review of daytime running lights was provided by Rumar (2003). Overall, the review indicated a rapidly increasing trend toward daytime lighting on both cars and powered two wheelers (PTWs) in Europe as well as in the other regions. The Association des Constructeurs EuropĂŠens de Motorcycles (ACEM) members already equip PTWs with automatic headlamp-on (AHO). In addition, riders are required to use headlamps during daytime in Denmark, Spain, France, Germany, Italy, Lithuania, Poland, Sweden, and Finland.

Brooks et al. (2005) conducted an experimental investigation to verify whether potential PTW conspicuity improvements could be studied in driving simulator experiments. Overall, the simulator methodology was found to be a powerful tool for examining differences in driver behavior and collision probability due to daytime lighting treatments in a sample of real-time crash scenarios.

A recent study assessed the accuracy of individuals being able to discriminate between the speeds of motorcycles and cars in daytime and nighttime conditions (Gould et al. 2011).

Computer simulations of different headlight configurations for motorcycles and cars approaching participants were used. The results demonstrated that individuals were significantly more accurate at judging the speed of the approaching car compared with both the solo and triheadlight motorcycles during the daytime condition.

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In addition, the results revealed that individuals were extremely poor at judging the speed of the motorcycle with a solo headlight during nighttime conditions. Conversely, in nighttime conditions, participants were significantly more accurate at judging the speed of the motorcycle equipped with a tri-headlight configuration.

Rӧger et al. (2012) conducted a laboratory experiment to show that motorcycles with a T-shaped light configuration were identified more quickly, particularly when the motorcycles are in visual competition with other motorized road users.

Another recent study investigated the effect of motorcyclist speed on “looked-but-fail-to-see” (LBFS) Crashes by precise kinematic reconstruction of 44 crash cases involving a motorcyclist and another road user at intersections (Clabaux et al. 2011). The results showed that, in urban environments, the initial speeds of motorcyclists involved in LBFS crashes were significantly higher than in other crashes. On the other hand, the difference in speed for motorcyclists between LBFS crashes and other crashes was not significant in rural environments.

Smither and Torres (2010) studied the effect of the age of car drivers and DRL on motorcycle conspicuity using a driving simulator and presenting video clips to participants with different combinations of headlight conditions (headlight on, off, and modulated) for motorcycles and vehicles following motorcycles (headlight on and off).

Seventy-five participants were asked to indicate when they saw motorcycles. Participant reaction times were collected and analyzed. Results revealed that younger adults were significantly faster than middle-aged and older adults to detect motorcycles. The results also revealed that males were faster than females at detecting motorcycles across all conditions.

Motorcycles with DRL were detected much faster than motorcycles with no lights and the difference was significant. The results also suggested that, when followed by a vehicle with low beams or DRLs on, a motorcycle that had its headlight on or headlights modulated were more quickly detected than a motorcycle without headlights or modulators on.

The study did not find any interaction effects between age and motorcycle lighting conditions.

One of the major limitations of this study was the absence of a complete driving task with participants seated in the simulator as if they were driving.

Rogé et al. (2012) studied whether the cognitive conspicuity and sensory conspicuity of car drivers were low when it came to detecting motorcycles. Forty-two car drivers (21 motorcyclists and 21 non-motorcyclists) carried out a motorcycle detection task in a car-driving simulator.

Results revealed that a high level of color contrast enhanced the visibility of motorcycles when the motorcycle appeared in front of the participants. Motorcyclist motorists detected oncoming

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motorcycles at a greater distance than non-motorcyclist motorists when motorcycles appeared from behind the participants.

Gershon et al. (2010) studied the effects of driving scenarios, motorcycle rider clothing (outfits), and motorcycle distance from the viewer on attention and search conspicuity of motorcycles by conducting two experiments. In the first experiment, compared to black outfits, reflective and white outfits increased motorcycle attention conspicuity when the background surrounding the motorcycle was more complex and multi-colored on urban roads. On the other hand, on interurban roads, black outfits provided an advantage for the motorcycle detectability when the background was solely a bright sky.

The same study results revealed that the average reaction time to identify the presence of a motorcycle was the shortest in the inter-urban environment.

In the second experiment, the detection rate for motorcycles, which represented search conspicuity, showed similar results to the first experiment on urban and inter-urban roads with respect to different outfits.

Lastly, the Motorcycle Safety Foundation (MSF) and Virginia Tech Transportation Institute (VTTI) are partnering on what is likely the world’s first large-scale, naturalistic motorcycle riding study. This study is underway and, once finished, will greatly enhance the understanding of interactions among rider, motorcycle, roadway, other roadway users, and environment.

One hundred motorcycles owned by riders in California, Florida, and Virginia are to be instrumented with highly-capable data acquisition systems (DASs) that use distributed cameras to collect data and investigate the sequence of events, actions of other vehicles, successful and unsuccessful maneuvers, types of events and frequency, and other factors.

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3. DATA COLLECTION AND DECRIPTION 3.1 Data Collection Procedure in NADS This chapter begins with a discussion of the experimental design and independent and dependent measures. The first section is followed by a description of the participant groups and experimental protocol, which includes participant recruitment and procedures, and the study visit by the participants to NADS. An overview of the experimental procedures is included in Appendix A. The methodology described here was utilized in the main data collection. An internal pilot using naïve staff as participants was utilized to test driving scenarios and the experimental protocol.

3.2 Experimental Design Thirty-six participants completed three drives on the NADS-2 driving simulator. During two of the drives, participants were presented several motorcycles, each with a different combination of rider color and headlight configuration. Participants indicated when each motorcycle was first visible to them by pressing a button on the steering wheel of the driving simulator. The following section discusses the independent variables and the dependent variables.

Two experimental designs were used for the driving simulator data as summarized in Table 3.1.

Table 3.1. Summary of driving simulator experimental designs Motorcycles Factors Oncoming Headlight configurations = modulating, DRL, high beam (3 factors) × rider color = black, bright yellow (2 factors) × environment = rural, urban (2 factors) Leading Rider color = black, blue denim, bright yellow (3 factors) × environment = rural, urban (2 factors) 3.2.1 Independent Variables The combination of the levels of independent variable resulted in six oncoming motorcycles and three leading parked motorcycles with each of the nine motorcycles present in both the urban and rural environments as shown in Figures 3.1 through 3.3.

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Figure 3.1. Simulated urban and rural environments

Figure 3.2. Two of the six oncoming rider and headlight combinations

Figure 3.3. The three combinations for leading parked motorcycles

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The oncoming motorcycles were part of traffic traveling in the opposite direction of the driver in the adjacent lane and, therefore, were visible to the driver from the front. The leading motorcycles were parked on the side of the road facing the same direction as the driver and, therefore, were visible to the driver from the rear.

While the locations within the drives of oncoming versus leading motorcycles were set, the order in which the six oncoming and the three leading motorcycle were presented at each location was randomized for participants.

3.2.1.1 Headlight Configurations Headlight configuration was presented at three levels: modulating, DRL, and high-beam. This independent variable was considered to investigate the impact of different configurations of headlights on motorcycle conspicuity in rural and urban environments.

The headlight configurations were implemented only on the motorcycles presented as oncoming traffic because, during pilot testing, the researchers confirmed that headlights do not contribute significantly to the conspicuity of motorcycles viewed from the rear, as is the case with leading motorcycles.

3.2.1.2 Rider Color Rider color can potentially affect the ability of other motorists to see motorcyclists. The objective was to investigate the impact of different rider colors on motorcycle conspicuity in urban and rural environments. Rider color consisted of both the motorcycle driver’s jacket and helmet color.

For oncoming motorcycles, rider color was presented in only black and bright yellow and for leading motorcycles, rider color was presented in black, blue denim, and bright yellow. Only the darkest and brightest colors were used for oncoming motorcycles because, during pilot testing, the researchers determined that headlight configuration was the dominant factor in the conspicuity of motorcycles viewed from the front. All motorcycle riders were presented with a pant color of blue denim.

3.2.1.3 Age of the Participants The addition of age group as an experimental variable was considered to investigate the differences in driver awareness of motorcyclists by age. Participants were within one of two groups: younger drivers (25 to 55) and older drivers (65 and older).

3.2.2 Dependent Variable Table 3.2 defines the dependent variable (adjusted detection distance) considered for this study.

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Table 3.2. Primary measures (dependent variable) Variable

Definition

Adjusted detection distance (ft)

Distance from the participant’s vehicle to motorcycles when he/she first sees the motorcycles and represented by the button press adjusted to account for each participant’s mean physical reaction time to press the button and their speed at the time of the button press

3.3 Test Devices The experimental drives were conducted on the NADS-2, a medium-fidelity, fixed-base driving simulator. The simulator consisted of a Jeep Cherokee cab equipped with active feel on steering, brake, and accelerator pedal and a fully operational dashboard. The audio subsystem consisted of speakers inside and outside the cab, which provided sound for wind, tire, engine, vehicle noise, passing traffic, and other environmental noises.

Driving data was collected at 240 Hz. The NADS-2 enhanced visual system has sufficient angular pixel resolution to satisfy 20/20 visual acuity. The commonly-accepted standard of 20/20 visual acuity is the ability to resolve 1 arc minute of detail. The projectors are 1,400 x 1,050 pixel digital light processing (DLP) projectors with five image generator channels and digital video (DVI) delivered over fiber optic cable. The three center channels are configured for high spatial resolution (0.5 arc minute/pixel) and two side channels are configured as lower spatial resolution and serve as larger field-of-view “expansion” channels. The three center channels have a field-of-view of 11.5 degrees vertically by 31 degrees horizontally, with the driver’s eye point 136 in. from the screen. The addition of the side channels results in a horizontal field-ofview of approximately 62 degrees.

3.4 Recruitment Method The primary recruitment tools were the NADS participant recruitment database, University of Iowa students, faculty and staff, and a listserv managed by the University of Iowa, College of Engineering.

The NADS database currently contains more than 5,500 names of potential participants that are interested in participating in driving studies conducted at NADS. A database query of the prospective age groups was completed. From this query, a list of names was generated, and potential participants were either sent an email message or phoned with information about the study. A recruitment email message was also sent to the University of Iowa community and the College of Engineering listserv that includes current students, faculty, staff, alumni, and friends of the college.

The recruitment materials provided NADS contact information for those interested in participating in the study (Appendix B). Those expressing interest were contacted by NADS staff.

10

A phone screening was conducted to determine if potential participants met study criteria (Appendix C). Those who met the study criteria were scheduled for participation at a study appointment.

3.5 Inclusion/Exclusion Criteria Potential participants had to be of an age that falls into one of the age groups in this study and meet general driving and health criteria to be enrolled. The general driving and health criteria are included in the telephone screening procedure. Participants were asked questions about their driving license and endorsements, driving experience, vision and restrictions while driving, previous simulator experience, and other general driving criteria. There were also questions for general health inclusion criteria in the Telephone Screening Procedure.

3.6 Simulator Drives Each participant completed a practice drive, a reaction-time task, and two study drives while in the driving simulator. The practice drive was approximately six minutes and allowed participants to become accustomed to the simulator.

Following the practice drive, participants completed the reaction-time test. During this task, the participant did not drive. Their physical reaction time from the appearance of a vehicle to press the button was measured for nine stationary vehicles, including some motorcycles, which were presented 60 to 80 feet in front of them in the oncoming lane. Each vehicle disappeared when the participant pressed the response button, followed by the next vehicle after a 1 to 3 second delay.

The mean reaction time from this task for each participant was used to adjust the detection distance to account for the distance they traveled between seeing a target vehicle and the actual button press.

Participants then completed each of the study drives, one in a rural environment and one in an urban environment (Figure 3.1). The posted speed limit was 55 mph in the rural environment and 35 mph in the urban environment. The drive in each environment was along a straight roadway with no obstructions or curves that would hinder the visual detection of a vehicle ahead. The order in which the urban and rural drives were completed was counter balanced across participants. The study drives lasted approximately 20 minutes each.

During each study drive, six oncoming motorcycles and three leading motorcycles were presented at predetermined locations. The sample images shown in Figures 3.2 and 3.3 illustrate the viewing angle and colors. In Figure 3.2, an example of the DRL setting for the headlight is also shown.

The order of both the oncoming and leading motorcycles was randomized for each participant.

Participants indicated when each motorcycle was first visible to them by pressing a button on the steering wheel of the driving simulator.

11

To reduce the potential for participants to become more vigilant than they would normally be toward motorcycles, participants were also asked to identify two other vehicle types, cargo and pick-up truck, during their drives. These other vehicles were present throughout the drives as part of the ambient traffic that included other vehicle models as well. These vehicles were placed so they did not interfere with the visual detection of the motorcycles.

3.7 Experimental Protocol Upon arrival for their study visit, informed consent was obtained for each participant (Appendix D). Video release and payment forms were completed; then, the Driving Survey was completed (Appendix E). The Driving Survey was reviewed by research staff to confirm inclusion criteria were met.

Vision testing followed the paper work (Appendix F). Immediately prior to entering the driving simulator, participants viewed the training PowerPoint that introduced the simulator and explained the tasks they would perform while in the simulator (Appendix G).

Once comfortably seated in the driving simulator with their seatbelt fastened, each participant completed the practice drive, the reaction-time test, and, then, each of the two study drives.

Following the simulator drives, participants completed surveys including the Motorcycle Conspicuity Related Factors (Appendix H). A debriefing statement was read to participants that clarified the purpose of the study and asked them not to reveal this purpose to anyone until a date when data collection was expected to be complete (Appendix I). This was done to maintain the consistency of expectations of participants across the data collection period.

3.8 Discussion of Descriptive Statistics of Participant Survey Data Questions were asked to acquire information about demographic factors, driving behavior, and driving comfort, and repeated driving history and health status of the participants from the NADS survey. A separate questionnaire was provided to the participants to rate different factors that might be potentially helpful to increase the conspicuity of motorcycles.

Table 3.3 shows the summary statistics for the demographic factors, driving habits, and licenserelated factors for both the younger (25 to 55) and older (65 and older) participants. In the interest of space, data is presented horizontally separated by slashes (such as 0.00/16.67/5.56/5.56/‌), rather than vertically (with only one value or set of data per line or table cell/column) in these tables. The values in parentheses in the tables provide the standard deviation (SD) values as indicated in their column headings.

12

Table 3.3. Descriptive statistics of participant survey data

Factors Gender (Male/female) Age Total household income (in 1,000s) 20-29/30-34.9/35-39/40-49/50-59/ 60-69/70-79/80-89/90-99/100 or more Driving starting age Commercial truck license Yes/No Motorcycle license Yes/No Other license Yes/No Driving frequency At least once daily/at least once weekly Work-related driving Yes/No Speed in residential area (mph)

Younger Participant (25-55 years) Mean (SD) or % 50/50 36.06(10.95) 0.00/16.67/5.56/5.56/ 11.11/5.56/33.33/5.56/ 5.56/11.11 15.11(2.2.29)

Older Participant (≼65 years) Mean (SD) or % 50/50 71.33(6.02) 5.56/0.00/0.00/11.11/ 22.22/5.56/0.00/11.11/ 11.11/33.33 14.75(2.14)

0/100

0/100

0/100

0/100

0/100

0/100

77.28/22.22

83.33/16.67

38.89/61.11

27.78/72.22

26.71(2.11)

25.75(1.77)

SD = standard deviation (in parentheses in this table data)

Distribution of the gender of the participants was even with half males and half females. Average age of the participants was 55.2 with a standard deviation of 18.54 including both younger and older drivers participating in the simulator study.

The researchers made sure none of the participants had a motorcycle or commercial truck driving license.

The average driving speed of the participants in residential environments was 26.71 mph for younger participants and 25.75 mph for older participants. Some demographic information of the participants is presented in pie charts for better visual representations later in this chapter.

Figures 3.4 through 3.7 show that almost two-thirds of the participants were married, half were full-time employees, and more than a third had a graduate degree. Nearly half of the participants drive 8,000 to 12,999 miles per year.

13

14% 14% Married Separated or divorced Single/Never married 72%

Figure 3.4. Marital status of the study participants

3%

8% Full time work Part time work Retired 50%

31%

Unemployed None

8%

Figure 3.5. Employment status of the study participants

14

Graduate degree 6%

Some graduate scool

20%

36%

Bachelor's Associates Some college or university High school diploma

8%

8% 22%

Figure 3.6. Educational qualifications of the study participants

8% 23% 23% 13,000 to 19,999 8,000 to 12,999 2,000 to 7,999 Under 2,000 46%

Figure 3.7. Yearly vehicle miles traveled by the study participants Table 3.4 summarizes participant responses to questions related to their driving behavior. The questions were asked to understand how frequently the participants executed certain tasks representing their driving behavior.

About 28 percent said they frequently exceeded the speed limit and 39 percent stated that they made left turns frequently at uncontrolled intersections.

15

Table 3.4. Participant responses about driving behavior questions from survey Questions Change lanes on interstate or highway Always/frequently/occasionally Keep up with traffic in town Always/frequently/occasionally Keep up with traffic in two-lane highway Always/frequently/occasionally Keep up with traffic on interstate or highway Always/frequently/occasionally Pass other cars on the interstate or freeway Always/frequently/rarely Wear a safety belt Always/frequently/occasionally Make left turns at uncontrolled intersections Always/frequently/occasionally/rarely/never

Driving Behavior (%) 22.22/33.33/44.44 50/42/8 52.8/41.7/5.6 44.4/47.2/5.6 11.1/41.7/5.6 88.9/5.6/2.8 13.9/38.9/27.8/11.1/2.8

Table 3.5 summarizes participant survey responses related to their driving comfort level with various situations/conditions. Most of the participants were comfortable driving on highways/freeways and the responses showed the majority of participants were more or less comfortable driving under most of the conditions asked about.

Table 3.5. Participant responses about driving comfort questions from survey Questions Driving on Highway/Freeway Very comfortable/slightly comfortable/slightly uncomfortable/very uncomfortable Driving with Children Very comfortable/slightly comfortable/slightly uncomfortable/very uncomfortable/not applicable Driving in High Density Traffic Very comfortable/slightly comfortable/slightly uncomfortable/very uncomfortable/not applicable Passing Other Cars Very comfortable/slightly comfortable/slightly uncomfortable/very uncomfortable Changing Lanes Very comfortable/slightly comfortable/slightly uncomfortable/very uncomfortable Making a Left Turn at Uncontrolled Intersections Very comfortable/slightly comfortable/slightly uncomfortable/very uncomfortable

16

Driving Comfort (%) 83.3/8.3/5.6/2.8

58.3/13.9/11.1/2.8/13.9

22.2/50/16.7/8.3/2.8

61.1/27.8/5.6/5.6

69.4/32/5.6/2.8

55.6/22.2/8.3/5.6

The participant driving survey also included questions about driving history (violations in the past five years). Table 3.6 shows the results of the responses to these questions. The majority of the participants did not receive a ticket for most of the traffic violations mentioned in the survey questions. However, nearly 28 percent of the participants were involved in a crash in the past five years.

Table 3.6. Participant response about driving history questions from survey

Questions Speeding 0 ticket/1 ticket/2 tickets Going too Slowly 0 ticket Failure to Yield Right of Way 0 ticket Disobeying Traffic Lights 0 ticket Disobeying Traffic Signs 0 ticket Improper Passing 0 ticket Improper Turning 0 ticket/1 ticket Reckless Driving 0 ticket Following another Car too Closely 0 ticket Driving while Intoxicated 0 ticket/1 ticket Any other Tickets other than the ones listed above Yes/No Crashes in the Past Five Years none/one

Driving History or Violations in past 5 years (%) 66.7/25/5.6 100 100 100 100 100 94.4/2.8 100 100 94.4/2.8 86.1/8.3 72.2/27.8

One interesting finding from the participant responses to the driving behavior questions was that almost 20 percent of the participants were used to exceeding the speed limit frequently while driving on the roadway. Figure 3.8 summarizes participant responses about how often they exceed the speed limit while driving.

17

3% 16%

28%

Always Frequently Occasionally Rarely

53%

Figure 3.8. Percentage of participants exceeding speed limit while driving More than half of the survey participants were slightly or very uncomfortable driving after drinking alcohol while a third of them indicated they don’t drive after drinking alcohol by responding not applicable. Figure 3.9 shows the distribution of the responses of the participants to the survey question.

Very comfortable 3%

11%

Sligltly comfortable Sligltly uncomfortable

33%

Very uncomfortable Not applicable 25%

28%

Figure 3.9. Participant driving comfort after drinking alcohol Participants were also asked to give their opinions about how helpful it was for them as drivers to see motorcyclists on the road when the riders used certain measures. Note that the survey was administered after the participants finished the simulator drive, so the conspicuity factors used in the experiment may have affected the responses.

18

Figure 3.10 shows the opinions of the participants about different measures taken by motorcyclists to improve conspicuity. The majority of participants considered the use of DRLs and modulating headlamps by motorcyclists as very helpful measures to increase conspicuity.

80.0

Very helpful

Percentage

70.0 60.0

Somewhat helpful

50.0 40.0

Neither helpful or unhelpful

30.0 20.0

Somewhat helpful

10.0 Hand signals

Modulating headlamp

Daytime running lights

Bright colored jacket

Fluorescent/Reflective safety vest

Bright colored helmet

Helmet

0.0 Very unhelpful

Figure 3.10. Participant opinions about the effectiveness of different measures to improve motorcycle conspicuity 3.9 Driving Simulator Data The two experimental designs for collecting the simulator data were a 3 (headlight configurations=modulating, DRL, high beam) × 2 (rider color=black, bright yellow) × 2 (environment=rural, urban) factorial experiment for oncoming motorcycles and a 3 (rider color=black, blue denim, bright yellow) × 2 (environment=rural, urban) factorial experiment for leading motorcycles in the simulator (as shown in Table 3.1).

There were four cases where participants either missed the motorcycles or saw the motorcycles but did not press the button completely. All of these participants were from the older age group and one of these missing cases occurred for the oncoming motorcycles and the rest for the leading motorcycles. These cases were treated as missing data and were discarded from further data analysis.

The leading and oncoming motorcycles with different combinations of rider colors and headlight configurations were presented to the participants in random order. The number of participant responses, equal to the number of detection distances recorded for oncoming and leading motorcycles, is detailed to account for the number of missing cases in Table 3.7.

19

Table 3.7. Number of responses by participants for oncoming and leading motorcycles Number of participants Experimental Factorial Original Sample Size Missing Cases Reduced Sample Size

Oncoming Motorcycles 36

Leading Motorcycles 36

1 431

3 213

The distribution of the detection distances for oncoming motorcycles seems to follow a normal distribution, while the distribution of the detection distances for leading motorcycles shows some positive skewness, as shown in Figures 3.11 and 3.12.

50 45 40

Frequency

35 30 25 20 15 10 5 200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000 2,200 2,400 2,600 2,800 3,000 3,200 3,400 3,600 3,800 4,000 4,200 4,400 4,600 4,800 5,000 More

0

Detection Distance (ft) for Oncoming Motorcycles

Figure 3.11. Distribution of the detection distances for oncoming motorcycles

20

40 35

Frequency

30 25 20 15 10 5 200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000 2,200 2,400 2,600 2,800 3,000 3,200 3,400 3,600 3,800 4,000 4,200 4,400 4,600 4,800 5,000 More

0

Detection Distance (ft) for Leading Motorcycle

Figure 3.12. Distribution of detection distances for leading motorcycles Besides the distributions of the overall detection distances for the two experimental designs (oncoming and leading motorcycles), the distribution of detection distances for each possible experimental condition was also examined for oncoming and leading motorcycles. With 12 experimental conditions for oncoming motorcycles and six for leading motorcycles, all of them showed distributions similar to those above for oncoming and leading motorcycles, so the researchers didn’t include them in this report.

Tables 3.8 and 3.9 shows the mean of the detection distances (in feet) recorded for oncoming and leading motorcycles for all the possible experimental conditions. It can be observed from the tables that the average detection distance of both oncoming and leading motorcycles for younger participants was higher than that for older participants for almost all the experimental conditions except for one (urban, bright yellow, modulating for oncoming motorcycles), where older participants detected oncoming motorcycles at a greater distance than the younger participants did.

21

Table 3.8. Mean and standard deviation of recorded detection distances for oncoming motorcycles given all possible experimental conditions Combination of Driving Environment, Rider Colors, and Headlights Rural, Black, DRL Rural, Black, High Beam Rural, Black, Modulating Rural, Bright Yellow, DRL Rural, Bright Yellow, High beam Rural, Bright Yellow, Modulating Urban, Black, DRL Urban, Black, High Beam Urban, Black, Modulating Urban, Bright Yellow, DRL Urban, Bright Yellow, High Beam Urban, Bright yellow, Modulating

Younger Participant (25-55 years) Mean (SD) ft 2,125.21(693.71) 2,766.31(1,173.60) 3,002.22(1,327.69) 2,318.90(647.22) 2,622.32(808.79) 3,647.33(1183.75) 2,102.36(490.82) 2,549.29(753.12) 3,068.90(1223.69) 2,298.39(809.43) 2,378.90 (1,023.17) 2,925.21(1,366.41)

Older Participant (≼65 years) Mean (SD) ft 2,026.51(505.67) 2,440.46(800.27) 2,837.28(920.53) 2,017.60(647.70) 2,232.55(688.56) 2,932.36(934.01) 1,938.84(621.38) 2,507.81(886.04) 2,987.36(1,008.43) 2,070.77(934.45) 2,162.60(931.94) 3,101.00(1,092.10)

SD = standard deviation (in parentheses in this table data)

Table 3.9. Mean and standard deviation of recorded detection distances for leading motorcycles given all possible experimental conditions Combination of Driving Environment and Rider Colors Rural, Black Rural, Blue Denim Rural, Bright Yellow Urban, Black Urban, Blue Denim Urban, Bright Yellow

Younger Participant (25-55 years) Mean (SD) ft 895.62(381.68) 1,232.14(550.86) 1,563.71(518.60) 1,075.09(532.81) 1,049.51(630.63) 2,143.20(1037.45)

SD = standard deviation (in parentheses in this table data)

22

Older Participant (≼65 years) Mean (SD) ft 713.60(298.54) 727.86(284.94) 1,450.96(353.97) 964.68(854.20) 1,124.51(1,068.13) 1,653.28(588.65)

4. METHODOLOGY 4.1 Generalized Linear Model (GLM) Repeated Measure Analysis of Variance The generalized linear model (GLM) repeated measure is a procedure used to model dependent variables measured at multiple times using analysis of variance. This is the method to use for analysis of variance when the same measurement is made several times on each subject or case (Garson, 2012).

If the between-subject factors are specified, the population can be divided into groups. Using GLM repeated measure analysis of variance, the null hypothesis about the effects of both the between-subjects factors and the within-subjects factors can be examined. It is also possible to investigate the interactions between factors, as well as the effect of individual factors. In a repeated measure analysis of variance, the effects of interest are as follows:   

Between-subject effects (such as between two different age groups of participants) Within-subject effects (such as rider color, headlight configurations, environment) Interaction between two or more types of effects (such as environment×rider color×headlight configuration)

GLM repeated measure analysis of variance was used to analyze the simulation data for the two experimental designs (one for oncoming and one for leading motorcycles). The inherent assumptions include linear relationships, normal distribution of the dependent variables, and fixed effects of the dependent variables (Garson, 2012). The two types of models to consider for the repeated measure analysis of variance are as follows:  

Univariate Multivariate

4.1.1 Univariate Model Univariate repeated measures models, also called split plot designs or mixed model designs, assume that the dependent variables are responses to the levels of a within-subject factor. For example, if a dependent variable y has p levels or measure outcomes and k predictor variables, for a single observation i, the linear model is as shown in equation (1). ( ) Equation (1) can be expressed more compactly by a set of matrices as follows.

(2)

23

where y is the matrix with dimension of (p×1), X is of size p×(1+k), β is of size (1+k)×1, and e is of size (p×1).

Or, alternatively:

[

]

[

][

]

[

]

(3)

The equation for the first measurement of the first observation is as follows: (4) In practical terms, three measures of y for a within-subject factor denoting y1, y2, and y3 can be considered and so a dummy-coded set of indicators for which y is in the data can be used. Three measures make two dummy-coded columns in X with one for y1 and one for y2 with y3 being the reference in this case. The intercept will be the mean of the measure y3, the first regression coefficient will be the difference between y1 and y3, and the second regression coefficient will be the difference between y2 and y3. The first observation including all the measurements for a subject can be expressed as follows:

[

]

[

][

]

[

]

(5)

Univariate models use an assumption called “sphericity,” which tests whether the variancecovariance matrix of the dependent variables can be shown to be circular in form. This is demonstrated by Mauchly’s test of sphericity (Huynh and Mandeville, 1979).

A spherical matrix has equal variances, and covariances equal to zero. A finding of nonsignificance in this test corresponds to concluding that assumptions are met for the univariate models.

Levene’s test examines the assumption of homogeneity of variance for univariate models. In a well-fitting model, the error variance of each repeated measure dependent variable should be the same across groups formed by the between-subject factors. Non-significance in this test implies that the assumption is met. Note that failure to meet the assumption of homogeneity is not critical if the assumption of sphericity is met.

24

4.1.2 Multivariate Model The multivariate tests assume a multivariate model where the dependent variables represent measurements of more than one variable for the different levels of the within-subject factors, unlike the univariate model (Garson, 2012).

The multivariate test provides F-tests of the within-subject factor and its interaction with the between-subjects grouping factors. Wilks’ lambda is the most commonly reported test statistic for the multivariate tests.

Multivariate models assume that the variance-covariance matrices are the same for each cell formed by the between-subject grouping variables. This is examined with the Box’s M test for the homogeneity of variance-covariance matrix (Box’s Test of Equality of Covariance Matrices).

This test also examines the assumption that the dependent variables in a multivariate model are drawn from a multivariate normal distribution. Non-significance in this test results in not rejecting the null hypothesis of observed variance covariance matrices of the dependent variables being equal across between-subject groups.

4.1.3 Hypothesis Testing Results are usually very similar for the univariate and multivariate tests. The F value for the analysis of variance is to be reported and higher F value compared to the critical F value will result in rejecting the null hypothesis.

For univariate analysis of variance, and for random samples Xl1, Xl2…, from N(µl, σ2) and l =1,…g, it is of interest to know if the population means of the groups are different; that is, if the model formulation is as follows: (6) with the constraint ∑

, which leads to the null hypothesis notation of the following:

Ho:

(7)

Then, the hypothesis is tested using equation (8): ⁄ ⁄∑

~Fg-1, ∑ -g(α)

(8)

where SSTr is the sum of squares for between-subject factors and SSRes is the sum of squares for the within-subject factors.

25

For multivariate analysis of variance, the null hypothesis is as follows: H0:

(9)

Then, the hypothesis is tested using equation (10). Λ=

(10)

Λ is called the Wilks’ Lambda and W and B are the sum of squares matrices for between-subject factors and within-subject factors, respectively. The null hypothesis is rejected if Λ is too small.

All hypotheses are considered to be significant at alpha = .05 in this study.

26

5. RESULTS The results are discussed in this chapter for both experimental designs: oncoming motorcycles and leading motorcycles. The dependent variable for all conditions evaluated was the detection distance (in feet).

The Box’s M Test showed non-significance for the dependent variables when oncoming motorcycles were considered; whereas, it was significant for the dependent variables when considering leading motorcycles.

The null hypothesis that the observed covariance matrices of the dependent variables are equal across groups could not be rejected for the oncoming motorcycles, while it was rejected for leading motorcycles. So, the results of the multivariate tests are reported for the oncoming motorcycles. For leading motorcycles, results of the univariate tests are reported after checking the assumption of Mauchly’s sphericity.

5.1 Detection Distance of Oncoming Motorcycles Table 5.1 presents the results of the multivariate analysis of variance for the detection distances of oncoming motorcycles. A finding of nonsignificance for the Box’s M test (p=0.249) supported the results of the multivariate tests. Wilks’ Lambda was used for interpretation of all the multivariate tests of significance unless otherwise noted. Table 5.2 presents the test results of the between-subject (age group) effects.

Multivariate tests revealed one significant main effect and two significant interaction effects. The main effect was found for headlight configurations. One interaction effect was found significant between rider colors and headlight configurations. Another interaction effect was found to be significant only at 90 percent confidence. No main effect for the age was found for the detection distances of oncoming motorcycles. This means the difference in detection distances of oncoming motorcycles for younger and older participants was not statistically significant.

The analysis indicated a significant main effect for headlight configurations, F (2, 32) = 45.83, p < .05, partial η2 = 0.741. The value of the partial η2 indicates almost 74 percent of the total variance in the detection distances is accounted for by the variance among the three different headlight configurations (DRL, high beam, and modulating) for oncoming motorcycles. Planned pairwise comparisons were conducted and headlight configuration was found to significantly affect detected distance measures, whereby motorcycles with high beam (mean = 2,437.15 ft and SD = 113.12 ft) were detected at greater distance than motorcycles with DRL (mean = 2,094.75 ft and SD = 84.85 ft) and motorcycles with the modulating headlight were detected at the greatest distance (mean = 3,075.6 ft and SD = 152.95 ft) compared to motorcycles with the DRL and the high beam at a level of p < 0.05 across all conditions. Figure 5.1 shows the detection distance as a function of motorcycle headlight configurations for oncoming motorcycles.

27

Table 5.1. Results of multivariate tests for oncoming motorcycles Effect F Environment .655 Environment × 1.243 AgeGroup RiderColor .171 RiderColor × AgeGroup 1.534 Headlight 45.826 Headlight × AgeGroup .136 Environment × 1.024 RiderColor Environment × .970 RiderColor × AgeGroup Environment × Headlight .508 Environment × Headlight 2.545 × AgeGroup RiderColor × Headlight 3.800 RiderColor × Headlight .039 × AgeGroup Environment × .995 RiderColor × Headlight Environment × 1.702 RiderColor × Headlight × AgeGroup

Hypothesis df 1.000 1.000

Error df 33.000 33.000

Sig. .424 .273

Partial Eta Squared .019 0.36

1.000 1.000 2.000 2.000 1.000

33.000 33.000 32.000 32.000 33.000

.682 .224 <.001** .874 .319

.005 .004 .741 .008 .030

1.000

33.000

.332

.029

2.000 2.000

32.000 32.000

.606 .094*

.031 .137

2.000 2.000

32.000 32.000

.033** .962

.192 .002

2.000

32.000

.381

.059

2.000

32.000

.198

.096

df = degree of freedom Sig. = Significance **significant at alpha = 0.05 *significant at alpha = 0.10

Table 5.2. Test of between-subject (age group) effects for oncoming motorcycles

Source Intercept AgeGroup Error

Type III Sum of Squares 224882240.349 459514.159 12969591.782

df 1 1 33

df = degree of freedom Sig. = Significance

28

Partial Eta Mean Square F Sig. Squared 224882240.349 572.193 .000 .945 459514.159 1.169 .287 .034 393017.933

Main Effect of Headlight Configuration 4,000

Detection Distance (ft)

3,500 3,000

3,075.60

2,500 2,437.15 2,000

2,094.75

1,500 1,000 500 DRL High Beam Modulating Headlight Configurations for Oncoming Motorcycles

Figure 5.1. Detection distance as a function of headlight configurations for oncoming motorcycles (means and standard errors) Results also revealed a significant interaction between rider colors and headlight configurations.

The interaction between these two factors influenced participant ability to detect a motorcycle significantly, F (2, 32) = 3.8, p < .05, partial Ρ2 = 0.65. The value of the partial Ρ2 = 0.65 indicates that 65 percent of the total variance in detection distance for oncoming motorcycles is accounted for by the variance due to the interaction among different levels of rider colors and headlight configurations.

Figure 5.2 shows the interaction between rider colors and headlight configurations. Results revealed when riders were wearing black or bright yellow (jacket and helmet), motorcycles with modulating headlights were detected at a greater distance compared to those with high beam or DRL.

29

Rider Color × Headlight Interaction 3,500 3,000

3,172.05

Detection Distance (ft)

2,979.15 2,500 2,000

2,546.33 2,327.98 2,143.60

2,045.90

DRL

1,500

High Beam Modulating

1,000 500 Black Bright Yellow Torso Colors for Oncoming Motorcycles

Figure 5.2. Detection distance as a function of motorcycle headlight configurations for rider colors of back and bright yellow (means and standard errors) 5.2 Detection Distance of Leading Motorcycles As mentioned in section 5.1, the Box’s M test resulted in (p = 0.15) significance leading to the rejection of null hypothesis that the variance covariance matrices of the dependent variables are equal across all between-subject factors. That is why the results of the univariate tests were reported for the detection distances of leading vehicles.

The main assumption for supporting the results of the univariate tests was examined by performing Mauchly’s sphericity test. The test resulted in nonsignificance (p = .262 for rider color and p = .895 for environment×rider color) and the results of the univariate tests were supported.

Table 5.3 presents the results of the univariate tests for leading motorcycles and Table 5.4 presents the results of the tests for between-subject effects. Given the Mauchly’s test showed no violation of sphericity, the interpretation of all univariate tests of significance was conducted under the assumption of sphericity.

30

Table 5.3. Results of univariate tests for leading motorcycles

Source Environment Environment×Age Error(Environment) TorsoColor TorsoColor×Age Error(TorsoColor) Environment×TorsoColor Environment×TorsoColor×Age Error(Environment×TorsoColor)

df 1 1 34 2 2 68 2 2 68

F 7.689 .283

Sig. .009** .598

Partial Eta Squared .184 .008

51.686 .431

.000** .652

.603 .013

1.110 3.098

.336 .052*

.032 .084

df = degree of freedom Sig. = Significance **significant at alpha = 0.05 *significant at alpha = 0.10

Table 5.4. Test of between-subject (age group) effects for leading motorcycles Source Intercept Age Error

Type III Sum of Squares 53246209.000 438170.448 5255160.997

df 1 1 34

Mean Square F 53246209.000 344.494 438170.448 2.835 154563.559

Sig. .000 .101

Partial Eta Squared .910 .077

df = degree of freedom Sig. = Significance

The analysis indicated a significant main effect for environment, F (1, 34) = 7.689, p < .05, partial η2 = 0.184. The value of the partial η2 indicates that 18.4 percent of the total variance in the detection distances is accounted for by the variance between the two different environments (rural versus urban) for leading motorcycles.

Planned pairwise comparisons were conducted and environmental condition was found to affect detected distance measures significantly, whereby motorcycles on urban roads (mean = 1,335.04 ft and SD = 96.21 ft) were detected at greater distance than motorcycles on rural roads (mean = 1,097.30 ft and SD = 54.82 ft) at a level of p < 0.05 across all conditions. Figure 5.3 shows the detection distance as a function of environment (rural and urban) for leading motorcycles.

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Main Effect of Environmental Condition 1,600

Detection Distance (ft)

1,400 1,335.04

1,200 1,000

1,097.30

800 600 400 200 Rural Urban Environmental Conditions for Leading Motorcycles

Figure 5.3. Detection distance as a function of environmental conditions (means and standard errors) The results revealed another main effect for rider color, F (2, 68) = 51.686, p < .05, partial Ρ2 = 0.603. The value of the partial Ρ2 indicates that 60 percent of the total variance in the detection distances is accounted for by the variance among the three different colors (black, blue denim, and bright yellow) of the riders for leading motorcycles.

Planned pairwise comparisons revealed that rider color affected detected distance measures significantly, whereby motorcycles with riders having blue denim color (mean = 1,033.5 ft and SD = 92.75 ft) were detected at greater distance than motorcycles with riders having black color (mean = 912.24 ft and SD = 69.72 ft) and motorcycles with riders having bright yellow color were detected at the greatest distance (mean = 1,702.80 ft and SD = 80.2 ft) at a level of p < 0.05 across all conditions. Figure 5.4 shows the detection distance as a function of rider color (black, blue denim, and bright yellow).

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Main Effect of Rider Color 2,500

Detection Distance (ft)

2,000 1,702.80

1,500

1,000

1,033.47 912.24

500

Black

Blue Denim Torso Color for Leading Motorcycles

Bright Yellow

Figure 5.4. Detection distance as a function of rider color (means and standard errors) A significant interaction among the environment, rider color, and age was found as well. The test was significant marginally at the alpha = 0.05 level. The interaction among these three factors influenced participant ability to detect a leading motorcycle significantly, F (2, 68) = 3.098, p < .10, partial η2 = 0.084. The value of the partial η2 = 0.084 indicates that only 8.4 percent of the total variance in detection distance for leading motorcycles is accounted for by the variance due to the interaction among different levels environment, rider colors, and age group of participants.

These effects were further explained through the use of planned pairwise comparison to evaluate the simple effects of these factors. An interaction effect was found between the conditions with a rider having the blue denim or bright yellow color and the age groups in rural or urban environments. The results suggest that, in a rural environment, the younger participants detected the motorcycles having blue denim rider color at a greater distance than older participants, F (1, 34) = 11.895, p < 0.05, partial η2 = 0.259. It is to be noted the results did not indicate significant differences between the detection distances for younger and older participants for riders with black and bright yellow colors in a rural environment.

The results also revealed that when a rider wore bright yellow clothing and helmet in an urban environment, the younger participants detected the motorcycles at a greater distance than older participants, F (1, 34) = 3.036, p < 0.10, partial η2 = 0.082. There were no significance differences between the detection distances for younger and older participants for riders with black and blue denim colors in an urban environment. Figure 5.5 shows this interaction effect and the boldfaced detection distances indicate that those are significantly different for younger and older participants.

33

2,800

2,800 Younger

Older

Younger 2,400

2,000 1,600 1,563.67 1,200 800

1,232.06 895.56 713.61

1,451.06

Detection Distance (ft)

Detection Distance (ft)

2,400

Older

2,000

2,143.17

1,600

1,653.28

1,200 800

1,075.06 964.72

727.83

400

400

-

-

1,124.56 1,049.44

Black Blue Denim Bright Yellow Torso Color in Urban Environment

Black Blue Denim Bright Yellow Torso Color in Rural Environemnt

Figure 5.5. Detection distance as a function of participant age group with blue denim and bright yellow riders in rural and urban environments (means and standard error) Furthermore, the study showed that older participants detected motorcycles with riders wearing a blue denim color at greater distance in the urban environment compared to the rural environment, F (1, 34) = 4.137, p < 0.05, partial Ρ2 = 0.108. Similar results were found for younger participants with riders wearing a bright yellow color, F (1, 34) = 6.783, p < 0.05, partial Ρ2 = 0.166. Figure 5.6 shows this interaction effect.

The boldfaced detection distances in the following figures indicate that they are statistically different from each other.

No statistically-significant difference was found between the detection distances of black and blue denim motorcycle riders in rural and urban environments by younger participants. There were also no statistically-significant differences between the detection distances of black and bright yellow riders in rural and urban driving environments by older participants.

34

2,000

Rural Urban

1,600 2,143.17

1,500

1,000

1,800

Rural Urban

1,653.28

1,400

Detection Distance (ft)

Detection Distance (ft)

2,500

1,563.67 1,232.06 1,075.06 1,049.44 895.56

1,451.06

1,200 1,124.56

1,000 800 600

964.72 713.61

727.83

400

500

200 Black

-

Blue Denim

Bright Yellow Torso Color-Younger Participants

Black

Blue Denim

Bright Yellow Torso Color-Older Participants

Figure 5.6. Detection distance as a function of the driving environment with bright yellow and blue denim riders for younger and older participants in rural and urban environments (means and standard error) The analysis results also revealed a significant interaction effect between the driving environments and rider colors for younger and older participants (Figures 5.7 and 5.8). For older participants, the detection distance for motorcycles with a rider wearing a bright yellow color was greater than blue denim and black in a rural environment, F (2, 33) = 29.216, p < 0.05, partial Ρ2 = 0.639. The same was true in an urban environment F (2, 33) = 5.263, p < 0.05, partial Ρ2 = 0.242. However, the results did not indicate statistical significance in the difference between detection distances for black and blue denim colors in either rural or urban environments.

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Environment×Rider Color Interaction for Older Age Group Participants 2,000

Detection Distance (ft)

1,800 1,600

1,653.28

1,400

1,451.06

1,200 1,000

1,124.56 964.72

800 600

Black Blue Denim

713.61 727.83

Bright Yellow

400 200 Rural

Urban Driving Environment

Figure 5.7. Detection distance as a function of rider color in rural and urban environments for older participants (means and standard error) Finally, for younger participants, detection distance for motorcycles with riders wearing a blue denim color was greater than for black. Detection distance for motorcycles with riders wearing a bright yellow color was greater than those for black and blue denim in a rural environment, F (2, 33) = 18.982, p < 0.05, partial η2 = 0.535.

For the urban environment, detection distance for motorcycles with riders wearing a bright yellow color was also greater than those for blue denim and black, F (2, 33) = 14.772, p < 0.05, partial η2 = 0.472. The results did not indicate a significant difference between detection distances for black and blue denim riders in an urban environment. Figure 5.8 shows this interaction.

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EnvironmentĂ&#x2014;Rider Color Interaction for Younger Age Group Participants 3,000

Detection Distance (ft)

2,500 2,000

2,143.17

1,500

Black

1,563.67

Blue Denim

1,232.06

1,000

1,049.44

895.56

Bright Yellow

1,075.06

500 Rural

Urban Driving Environment

Figure 5.8. Detection distance as a function of rider color in rural and urban environments for younger participants (means and standard error)

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6. CONCLUSIONS AND RECOMMENDATIONS 6.1 Conclusions This research investigated the impact of motorcycle headlight configurations, rider colors, and age of the drivers (participants) on motorcycle conspicuity in simulated urban and rural environments. The recorded detection distances from the oncoming and leading motorcycles to the participant vehicles were used to analyze the simulator data. Motorcycles detected at greater distances by study participants were considered more conspicuous.

The GLM repeated measure analysis of variance was used to determine the effect of headlight configurations and rider clothing and helmet colors on the conspicuity of motorcycles in urban and rural environments to younger and older driver (participant) groups.

The analysis was conducted separately for oncoming and leading motorcycles. It must be noted that the implications of the detection distances reported in this study are relative rather than absolute as this study was conducted in a simulated environment.

Overall, the study results revealed that motorcycles with modulating headlights had longer detection distances than high beams or DRLs by both younger and older drivers in both urban and rural environments. The results also indicated that motorcycle riders wearing bright yellow clothing and helmets were detected at the longest distance by both younger and older drivers in both rural and urban environments.

6.1.1. For Oncoming Motorcycles The analysis revealed a significant main effect for the headlight configurations (DRL, high beam, modulating) on detection distances. Motorcycles with modulating headlights were detected at the greatest distance and motorcycles with high beams were detected at a greater distance than motorcycle with DRLs by the study participants.

No significant main effect of the participant age groups on the detection distances was found. In addition, the driving environment (rural versus urban) did not have a significant main effect on the recorded detection distances.

A significant interaction between rider colors and headlight configurations was found.

Participant ability to detect a motorcycle was significantly influenced by the headlight configurations when the motorcycles had black or bright yellow riders.

As expected, motorcycles with modulating headlights were detected at the greatest distance (compared to motorcycles with high beam and the DRL) for both cases where the riders had black and bright yellow rider colors.

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6.1.2 For Leading Motorcycles The analysis revealed a significant main effect for driving environment (rural versus urban) on detection distances. Motorcycles in urban environments were detected at a greater distance compared to those in rural environments.

The analysis results also revealed another main effect for rider color on the detection distances.

As hypothesized, motorcycles with riders wearing blue denim were detected at a greater distance than those with riders wearing black. Motorcycles with riders wearing bright yellow were detected at the greatest distance by the study participants.

A significant interaction effect among the driving environment, rider color, and age group was also found for the leading motorcycles. Younger participants detected motorcycles with riders having bright colors (blue denim and bright yellow) at a greater distance than older participants in both urban and rural environments. So, it can be said that older participants might have more difficulty than younger participants in detecting leading motorcycles.

Furthermore, the researchers found that both younger and older participant groups detected motorcycles with riders having bright colors (blue denim and bright yellow) at a greater distance in the urban environment than in the rural environment. So, it can be concluded that on urban roads, where the background surrounding of the motorcycles is more complex and multi-colored, bright outfits can increase motorcycle conspicuity compared to black outfits.

6.2 Recommendations In view of the analysis results, the following recommendations might be considered in implementing motorcycle conspicuity-related campaigns and interventions:    

The conspicuity of a motorcycle can be increased by using an appropriate rider outfit (bright) that distinguishes them from the surrounding background Using a modulating headlight on a motorcycle can increase the conspicuity of a motorcycle significantly, irrespective of the background environment Increasing the alertness and expectancy of drivers to the presence of motorcycles can increase conspicuity, as the study revealed that motorcycles were detected at greater distances in an urban environment compared to a rural environment Awareness programs targeted specifically to older drivers can be considered

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APPENDIX A. EXPERIMENTAL PROCEDURES

43

APPENDIX B. EMAIL SCRIPT

44

APPENDIX C. TELEPHONE SCREENING PROCEDURES

45

46

47

48

49

50

APPENDIX D. INFORMED CONSENT DOCUMENT

51

52

53

54

55

56

APPENDIX E. DRIVING SURVEY

57

58

59

60

61

62

APPENDIX F. VISION TEST FORM

63

64

65

APPENDIX G. PARTICIPANT TRAINING

66

67

68

69

70

71

72

73

APPENDIX H. MOTORCYCLE CONSPICUITY-RELATED FACTORS SURVEY

74

75

APPENDIX I. DEBRIEFING STATEMENT

76